Out Of Data Research Articles

Oral lichen planus: case series and experience in a tertiary dermatology service in Brazil

BackgroundLichen planus is an inflammatory disease that can affect both the skin and mucous membranes, including the oral mucosa. There is very little original Brazilian dermatology literature about oral lichen planus. ObjectiveTo describe the clinical, pathological, and treatment data of 201 patients diagnosed with oral lichen planus followed at the Stomatology Outpatient Clinic of Hospital das Clínicas, Universidade de São Paulo, from 2003 to 2021. MethodThe patients demographic profile, the morpho-topographic features of the lesions, the treatment employed, and the possible presence of squamous cell carcinoma were analyzed. ResultsThe disease was more common in women over 50 years of age, tending to be chronic, with a large number of cases showing cicatricial sequelae in the mucosa. Topical treatment with potent corticosteroids was shown to be effective in the vast majority of cases. Squamous cell carcinoma in oral lichen planus cicatricial sequelae was observed in eight cases. Study limitationsRetrospective study of medical records, with gaps regarding the filling out of data; unequal observation time among the studied cases. ConclusionsThis is the largest Brazilian dermatology series on oral lichen planus. The response to topical corticoid therapy was excellent in the vast majority of cases. The high prevalence of atrophic lesions, demonstrating the chronicity and tissue destruction potential of this disease, may explain the large number of cases of squamous cell carcinoma.

Machine-Learned Electronically Excited States with the MolOrbImage Generated from the Molecular Ground State.

We present a general machine learning framework for probing the electronic state properties using the novel quantum descriptor MolOrbImage. Each pixel of the MolOrbImage records the quantum information generated by the integration of the physical operator with a pair of bra and ket molecular orbital (MO) states. Inspired by the success of deep convolutional neural networks (NNs) in computer vision, we have implemented the convolutional-layer-dominated MO-NN model. Using the orbital energy and electron repulsion integral MolOrbImages, the MO-NN model achieves promising prediction accuracies against the ADC(2)/cc-pVTZ reference for transition energies to both low-lying singlet [mean absolute error (MAE) < 0.16 eV] and triplet (MAE < 0.14 eV) states. An apparent improvement in the prediction of oscillator strength, which has been shown to be challenging previously, has been demonstrated in this study. Moreover, the transferability test indicates the remarkable extrapolation capacity of the MO-NN model to describe the out of data set systems.

Intelligent Petroleum Engineering

Data-driven approaches and artificial intelligence (AI) algorithms are promising enough to be relied on even more than physics-based methods; their main feed is data which is the fundamental element of each phenomenon. These algorithms learn from data and unveil unseen patterns out of it. The petroleum industry as a realm where huge volumes of data are generated every second is of great interest to this new technology. As the oil and gas industry is in the transition phase to oilfield digitization, there has been an increased drive to integrate data-driven modeling and machine learning (ML) algorithms in different petroleum engineering challenges. ML has been widely used in different areas of the industry. Many extensive studies have been devoted to exploring AI applicability in various disciplines of this industry; however, lack of two main features is noticeable. Most of the research is either not practical enough to be applicable in real-field challenges or limited to a specific problem and not generalizable. Attention must be given to data itself and the way it is classified and stored. Although there are sheer volumes of data coming from different disciplines, they reside in departmental silos and are not accessible by consumers. In order to derive as much insight as possible out of data, the data needs to be stored in a centralized repository from where the data can be readily consumed by different applications.